This (un)certainty is reflected in prior distributions
What priors say about the distribution of (unobserved) data can be hard to grok
\(\therefore\) use prior predictive simulation
What priors say about the distribution of (unobserved) data can be hard to grok
\(\therefore\) use prior predictive simulation
To the extent parameters are meaningful/interpretable (e.g., a slope) you can:
What parameters say about expectations on the response scale can be hard to grok
\(\therefore\) plot posterior inference (e.g., against data)
What parameters say about expectations on the responses scale can be hard to grok
\(\therefore\) plot posterior inference (e.g., against data)
Involves parameter uncertainty + sampling noise.
What they say about the distribution of (future) data can be hard to grok
\(\therefore\) use posterior predictive simulation
What they say about the distribution of (future) data can be hard to grok
\(\therefore\) use posterior predictive simulation
As always, the key questions is what do you want to know?
Just be clear about the uncertainty you are or are not showing/including